乐胖代购免代理版

{"id":"https://openalex.org/W2087831740","doi":"https://doi.org/10.1109/icmla.2011.117","title":"Predictive Subspace Clustering","display_name":"Predictive Subspace Clustering","publication_year":2011,"publication_date":"2011-12-01","ids":{"openalex":"https://openalex.org/W2087831740","doi":"https://doi.org/10.1109/icmla.2011.117","mag":"2087831740"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla.2011.117","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041664883","display_name":"Brian McWilliams","orcid":"https://orcid.org/0009-0002-7433-1702"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"funder","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Brian McWilliams","raw_affiliation_strings":["Dept. of Math., Imperial Coll. London, London, UK#TAB#"],"affiliations":[{"raw_affiliation_string":"Dept. of Math., Imperial Coll. London, London, UK#TAB#","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010581004","display_name":"Giovanni Montana","orcid":"https://orcid.org/0000-0003-3942-3900"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"funder","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Giovanni Montana","raw_affiliation_strings":["Dept. of Math., Imperial Coll. London, London, UK#TAB#"],"affiliations":[{"raw_affiliation_string":"Dept. of Math., Imperial Coll. London, London, UK#TAB#","institution_ids":["https://openalex.org/I47508984"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.759,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":4,"citation_normalized_percentile":{"value":0.453115,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":78,"max":80},"biblio":{"volume":null,"issue":null,"first_page":"247","last_page":"252"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.999,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.999,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.996,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9953,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.6212483},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.55988795}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8162352},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.78983194},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.74500775},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7045861},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.6212483},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.567038},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5643456},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.55988795},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5352355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47182828},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43495673},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.43053475},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.41895506},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35724688},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22147086},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla.2011.117","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":15,"referenced_works":["https://openalex.org/W1533128638","https://openalex.org/W2003217181","https://openalex.org/W2013712253","https://openalex.org/W2048997388","https://openalex.org/W2094265461","https://openalex.org/W2095843685","https://openalex.org/W2123921160","https://openalex.org/W2128917343","https://openalex.org/W2132914434","https://openalex.org/W2134507164","https://openalex.org/W2148694408","https://openalex.org/W2294644361","https://openalex.org/W3100720754","https://openalex.org/W4250657332","https://openalex.org/W4250857377"],"related_works":["https://openalex.org/W4362679532","https://openalex.org/W4293428697","https://openalex.org/W2977728051","https://openalex.org/W2968737478","https://openalex.org/W2465328940","https://openalex.org/W227974271","https://openalex.org/W2185081213","https://openalex.org/W2104708299","https://openalex.org/W2103268563","https://openalex.org/W2094490861"],"abstract_inverted_index":{"The":[0,153],"problem":[1],"of":[2,25,30,42,48,52,73,102,107,136,139],"detecting":[3],"clusters":[4,39,103],"in":[5,11,17,27,40,70,110,120,126],"high-dimensional":[6],"data":[7,118,140,164],"is":[8,59,159],"increasingly":[9],"common":[10],"machine":[12],"learning":[13],"applications,":[14],"for":[15,38],"instance":[16],"computer":[18],"vision":[19],"and":[20,55,63,79,104,165,175],"bioinformatics.":[21],"Recently,":[22],"a":[23,60,92,129,134],"number":[24,47],"approaches":[26],"the":[28,46,50,56,71,100,105,117,150,167],"field":[29],"subspace":[31,96,109,156],"clustering":[32,97,151,157],"have":[33,76],"been":[34],"proposed":[35,154],"which":[36,145],"search":[37],"subspaces":[41,74],"unknown":[43],"dimensions.":[44],"Learning":[45],"clusters,":[49],"dimension":[51],"each":[53,108,121],"subspace,":[54],"correct":[57],"assignments":[58],"challenging":[61],"task,":[62],"many":[64],"existing":[65],"algorithms":[66],"often":[67],"perform":[68],"poorly":[69],"presence":[72],"that":[75,98,116],"different":[77],"dimensions":[78],"possibly":[80],"overlap,":[81],"or":[82],"are":[83,123,177],"otherwise":[84],"computationally":[85],"expensive.":[86],"In":[87],"this":[88],"work":[89],"we":[90,146],"present":[91],"novel":[93],"approach":[94],"to":[95,148],"learns":[99],"numbers":[101],"dimensionality":[106],"an":[111],"efficient":[112],"way.":[113],"We":[114,132],"assume":[115],"points":[119,141],"cluster":[122],"well":[124],"represented":[125],"low-dimensions":[127],"by":[128,143],"PCA":[130,144],"model.":[131],"propose":[133],"measure":[135],"predictive":[137,155],"influence":[138],"modelled":[142],"minimise":[147],"drive":[149],"process.":[152],"algorithm":[158],"assessed":[160],"on":[161,166],"both":[162],"simulated":[163],"popular":[168],"Yale":[169],"faces":[170],"database":[171],"where":[172],"state-of-the-art":[173],"performance":[174],"speed":[176],"obtained.":[178]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2087831740","counts_by_year":[{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2025-01-24T07:32:30.869061","created_date":"2016-06-24"}